Making ‘Glossy’ Networks Sparkle: Exploiting Concurrent Transmissions for Energy Efficient, Reliable, Ultra-Low Latency Communication in Wireless Control Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Dingwen Yuan , Michael Riecker , Matthias Hollick

ABSTRACT

Wireless sensor networks (WSN) offer a promising engineering solution which brings deployment flexibility and saves wiring costs in industrial automation and control applications. However, utilizing this technology is still challenging due to the current low reliability of the standard WSN communication, e.g., large percentage of unreliable links, and the possibility of battery depletion and node failure. To overcome these difficulties we propose Sparkle, a WSN control network design based on concurrent transmission particularly oriented to periodic multi-loop control systems. It primarily draws on the Glossy protocol and inherits its advantages in high packet reliability, low latency and accurate time synchronization. The novelty of Sparkle is that it optimizes each end-to-end communication flow independently by performing “control” on the flow. Specifically, we show how to perform transmission power and topology controls. We introduce WSNShape, a unique topology control technique based on the capture effect, which drastically reduces energy while simultaneously improves packet reliability and latency, compared to Glossy. Finally, we design and implement PRRTrack, a component of Sparkle that can adaptively switch among various operation modes of different control strategies, thus trading-off reliability vs. energy consumption. Through evaluation on real-world testbeds, we demonstrate that Sparkle enables flows to satisfy the reliability requirement while reducing the energy consumption by in average 80% and the latency by a further 5% over the almost optimal latency of Glossy. More... »

PAGES

133-149

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-04651-8_9

DOI

http://dx.doi.org/10.1007/978-3-319-04651-8_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017367724


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